Automated performance checks for autonomous vehicles

ABSTRACT

Aspects of the disclosure provides for a method for performing checks for a vehicle. In this regard, a plurality of performance checks may be identified including a first check for a detection system of a plurality of detection systems of the vehicle and a second check for map data. A test route for the vehicle may be determined based on a location of the vehicle and the plurality of performance checks. The vehicle may be controlled along the test route in an autonomous driving mode, while sensor data may be received from the plurality of detection systems of the vehicle. An operation mode may be selected based on results of the plurality of performance checks, and the vehicle may be operated in the selected operation mode.

BACKGROUND

Autonomous vehicles, such as vehicles which do not require a humandriver when operating in an autonomous driving mode, may be used to aidin the transport of passengers or items from one location to another. Animportant component of an autonomous vehicle is the perception system,which allows the vehicle to perceive and interpret its surroundingsusing cameras, radar, sensors, and other similar devices. The perceptionsystem executes numerous tasks while the autonomous vehicle is inmotion, which ultimately leads to decisions, such as speeding up,slowing down, stopping, turning, etc. The perception system may includea plurality of detection systems, such as cameras, sensors, and globalpositioning devices, which gathers and interprets images and sensor dataabout its surrounding environment, e.g., parked cars, trees, buildings,etc.

SUMMARY

Aspects of the disclosure provides for a system comprising one or morecomputing devices configured to identify a plurality of performancechecks including a first check for a detection system of a plurality ofdetection systems of the vehicle and a second check for map data; selecta plurality of road segments based on a location of the vehicle and theplurality of performance checks, wherein each of the plurality of roadsegments is selected for performing one or more of the plurality ofperformance checks; determine a test route for the vehicle by connectingthe plurality of road segments and by connecting the location of thevehicle to one of the plurality of road segments; control the vehiclealong the test route in an autonomous driving mode; while controllingthe vehicle, receive sensor data from the plurality of detection systemsof the vehicle; perform the plurality of performance checks based on thereceived sensor data; select an operation mode from a plurality ofoperation modes for the vehicle based on results of the plurality ofperformance checks; and operate the vehicle in the selected operationmode.

The plurality of road segments may include a first road segment, whereinone or more of the plurality of performance checks may be performedusing one or more traffic features or stationary objects that aredetectable along the first road segment. The plurality of road segmentsmay include a second road segment on which a maneuver required for oneor more of the plurality of performance checks can be performed.

The first check may include comparing characteristics of a detectedtraffic feature with previously detected characteristics of the trafficfeature. The second check may include comparing a location of a detectedtraffic feature with a location of the detected traffic feature in themap data.

The plurality of performance checks may further include a third checkfor a component of the vehicle, the third check may include comparingone or more measurements related to the component of the vehicle with athreshold measurement.

The operation mode may be selected based on the results satisfying athreshold number of the plurality of performance checks. The operationmode may be selected based on the results satisfying one or more set ofperformance checks of the plurality of performance checks.

The one or more computing devices may be further configured to determineone or more corrections to at least one of the detection systems basedon the results of the plurality of performance checks. Operating in theselected operation mode may include using the one or more corrections.

The one or more computing devices may be further configured to updatethe map data based on the results of the plurality of performancechecks. Operating in the selected operation mode may include using theupdated map data.

The selected operation mode may be an inactive mode.

The system may further comprise the vehicle.

The disclosure further provides for identifying, by one or morecomputing devices, a plurality of performance checks including a firstcheck for a detection system of a plurality of detection systems of thevehicle and a second check for map data; selecting, by the one or morecomputing devices, a plurality of road segments based on a location ofthe vehicle and the plurality of performance checks, wherein each of theplurality of road segments is selected for performing one or more of theplurality of performance checks; determining, by the one or morecomputing devices, a test route for the vehicle by connecting theplurality of road segments and by connecting the location of the vehicleto one of the plurality of road segments; controlling, by the one ormore computing devices, the vehicle along the test route in anautonomous driving mode; while controlling the vehicle, receiving, bythe one or more computing devices, sensor data from the plurality ofdetection systems of the vehicle; performing, by the one or morecomputing devices, the plurality of performance checks based on thereceived sensor data; selecting, by the one or more computing devices,an operation mode from a plurality of operation modes for the vehiclebased on results of the plurality of performance checks; and operating,by the one or more computing devices, the vehicle in the selectedoperation mode.

The plurality of road segments may include a first road segment, whereinone or more of the plurality of performance checks may be performedusing one or more traffic features or stationary objects that aredetectable along the first road segment. The plurality of road segmentsmay include a second road segment on which a maneuver required for oneor more of the plurality of performance checks can be performed.

The method may further comprise determining, by the one or morecomputing devices, one or more corrections to at least one of thedetection systems based on the results of the plurality of performancechecks, wherein operating in the selected operation mode may includeusing the one or more corrections. The method may further compriseupdating, by the one or more computing devices, the map data based onthe results of the plurality of performance checks, wherein operating inthe selected operation mode may include using the updated map data.

The plurality of performance checks may be performed at a regularinterval.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional diagram of an example vehicle in accordance withaspects of the disclosure.

FIG. 2 is an example representation of map data in accordance withaspects of the disclosure.

FIG. 3 is an example external view of a vehicle in accordance withaspects of the disclosure.

FIG. 4 is an example pictorial diagram of a system in accordance withaspects of the disclosure.

FIG. 5 is an example functional diagram of a system in accordance withaspects of the disclosure.

FIG. 6 is an example situation in accordance with aspects of thedisclosure.

FIG. 7 shows examples of collected sensor data in accordance withaspects of the disclosure.

FIG. 8 show examples of collected component data in accordance withaspects of the disclosure.

FIG. 9 show another example situation in accordance with aspects of thedisclosure.

FIG. 10 is an example flow diagram in accordance with aspects of thedisclosure.

DETAILED DESCRIPTION

Overview

The technology relates to performance checks for a vehicle to beperformed after a full calibration, prior to operation, or at regularintervals. Before operating a vehicle on the road, a human driver maycheck various systems and components of the vehicle, such as making surethat the mirrors are adjusted, that the GPS system is functioning, andcomponents such as steering wheel, brake, and signal lights, areresponsive. Likewise, various systems of an autonomous vehicle also needto be checked before operation, particularly when the vehicle is to beoperated in an autonomous mode, where a human driver may not be presentto notice problems with the vehicle's systems. For instance, even if thevehicle had been fully calibrated in the past, a sensor in a perceptionsystem of the vehicle might have been moved during previous operationsuch as by another road user or a cleaner, or have been damaged byenvironmental factors such as temperature, humidity, etc.

As such, a plurality of performance checks may be performed on thevehicle including, for example, a sensor check, a map check, and/or acomponent check. The sensor check may include determining a level offunction of a given sensor or detection system, such as detectionaccuracy, detection resolution, field of view, etc. The map check mayinclude determining an accuracy of the map data in relation to a givengeographic area. The component check may include determining a level offunction of a given component, such as tire pressure, tire alignment,etc. The results of the plurality of performance checks may be used todetermine what functions of the vehicle are within set guidelines, suchas for safety and comfort. The results may also be used to designate orclear the vehicle for particular modes of operation.

To perform the plurality of performance checks, one or more computingdevices may determine a test route based on the location of the vehicle,the map data, and the plurality of performance checks for the pluralityof systems of the vehicle. The test route need not include a designateddepot or testing center, or be a closed route.

The vehicle's computing devices may navigate the vehicle along the testroute using the one or more components and collect data using theplurality of detection systems. Collecting the data may include using adetection system of the plurality of detection system to detect one ormore traffic features or stationary objects along the test route. Inaddition, collecting the data may include detecting one or moremeasurements related to a component of the vehicle.

During the test route or after the vehicle completes the test route, thevehicle's computing devices may perform the plurality of performancechecks by analyzing collected data. For a sensor check, characteristicsof a detected traffic feature (such as location, orientation, shape,color, reflectivity, etc.) may be compared with previously detected orstored characteristics of the traffic feature. For a map check, alocation or orientation of a detected traffic feature may be comparedwith the location or orientation of a previously detected or storedtraffic feature in map data of the vehicle. For a component check, theone or more measurements related to a component of the vehicle may becompared with a threshold measurement.

Based on results from the plurality of performance checks, such as basedon which performance checks have been satisfied, the vehicle's computingdevices may select an operation mode for operating the vehicle.Operation modes may include, for example, task designations (passengeror non-passenger tasks), or limits on speeds, distance, or geographicarea. Operation modes may also include an inactive mode, for example ifthe vehicle is not cleared for any other mode. In some implementations,modes may be selected for a plurality of vehicles by a remote system,such as a fleet management system. The vehicle may then be operated bythe vehicle's computing devices in a particular mode based on theplurality of performance checks.

The features described above may allow autonomous vehicles to be quicklyand properly prepared for operation. Quicker preparation means vehiclesmay be sent to users in a more timely fashion, even as demandfluctuates. As a result, users of autonomous vehicles may be able to bepicked up in a timely manner. In addition, fewer resources, such asfuel, need be used in the preparation of the autonomous vehicle forservice, which may reduce overall costs. The features also allow formanagement of an entire fleet of autonomous vehicles designated for aplurality of modes that may service users more efficiently and safely.

Example Systems

As shown in FIG. 1, a vehicle 100 in accordance with one aspect of thedisclosure includes various components. While certain aspects of thedisclosure are particularly useful in connection with specific types ofvehicles, the vehicle may be any type of vehicle including, but notlimited to, cars, trucks, motorcycles, busses, recreational vehicles,etc. The vehicle may have one or more computing devices, such ascomputing device 110 containing one or more processors 120, memory 130and other components typically present in general purpose computingdevices.

The memory 130 stores information accessible by the one or moreprocessors 120, including instructions 132 and data 134 that may beexecuted or otherwise used by the processor 120. The memory 130 may beof any type capable of storing information accessible by the processor,including a computing device-readable medium, or other medium thatstores data that may be read with the aid of an electronic device, suchas a hard-drive, memory card, ROM, RAM, DVD or other optical disks, aswell as other write-capable and read-only memories. Systems and methodsmay include different combinations of the foregoing, whereby differentportions of the instructions and data are stored on different types ofmedia.

The instructions 132 may be any set of instructions to be executeddirectly (such as machine code) or indirectly (such as scripts) by theprocessor. For example, the instructions may be stored as computingdevice code on the computing device-readable medium. In that regard, theterms “instructions” and “programs” may be used interchangeably herein.The instructions may be stored in object code format for directprocessing by the processor, or in any other computing device languageincluding scripts or collections of independent source code modules thatare interpreted on demand or compiled in advance. Functions, methods androutines of the instructions are explained in more detail below.

The data 134 may be retrieved, stored or modified by processor 120 inaccordance with the instructions 132. As an example, data 134 of memory130 may store predefined scenarios. A given scenario may identify a setof scenario requirements including a type of object, a range oflocations of the object relative to the vehicle, as well as otherfactors such as whether the autonomous vehicle is able to maneuveraround the object, whether the object is using a turn signal, thecondition of a traffic light relevant to the current location of theobject, whether the object is approaching a stop sign, etc. Therequirements may include discrete values, such as “right turn signal ison” or “in a right turn only lane”, or ranges of values such as “havingan heading that is oriented at an angle that is 30 to 60 degrees offsetfrom a current path of vehicle 100.” In some examples, the predeterminedscenarios may include similar information for multiple objects.

The one or more processor 120 may be any conventional processors, suchas commercially available CPUs. Alternatively, the one or moreprocessors may be a dedicated device such as an ASIC or otherhardware-based processor. Although FIG. 1 functionally illustrates theprocessor, memory, and other elements of computing device 110 as beingwithin the same block, it will be understood by those of ordinary skillin the art that the processor, computing device, or memory may actuallyinclude multiple processors, computing devices, or memories that may ormay not be stored within the same physical housing. As an example,internal electronic display 152 may be controlled by a dedicatedcomputing device having its own processor or central processing unit(CPU), memory, etc. which may interface with the computing device 110via a high-bandwidth or other network connection. In some examples, thiscomputing device may be a user interface computing device which cancommunicate with a user's client device. Similarly, the memory may be ahard drive or other storage media located in a housing different fromthat of computing device 110. Accordingly, references to a processor orcomputing device will be understood to include references to acollection of processors or computing devices or memories that may ormay not operate in parallel.

Computing device 110 may have all of the components normally used inconnection with a computing device such as the processor and memorydescribed above as well as a user input 150 (e.g., a mouse, keyboard,touch screen and/or microphone) and various electronic displays (e.g., amonitor having a screen or any other electrical device that is operableto display information). In this example, the vehicle includes aninternal electronic display 152 as well as one or more speakers 154 toprovide information or audio visual experiences. In this regard,internal electronic display 152 may be located within a cabin of vehicle100 and may be used by computing device 110 to provide information topassengers within the vehicle 100. In addition to internal speakers, theone or more speakers 154 may include external speakers that are arrangedat various locations on the vehicle in order to provide audiblenotifications to objects external to the vehicle 100.

In one example, computing device 110 may be an autonomous drivingcomputing system incorporated into vehicle 100. The autonomous drivingcomputing system may capable of communicating with various components ofthe vehicle. For example, computing device 110 may be in communicationwith various systems of vehicle 100, such as deceleration system 160(for controlling braking of the vehicle), acceleration system 162 (forcontrolling acceleration of the vehicle), steering system 164 (forcontrolling the orientation of the wheels and direction of the vehicle),signaling system 166 (for controlling turn signals), navigation system168 (for navigating the vehicle to a location or around objects),positioning system 170 (for determining the position of the vehicle),perception system 172 (for detecting objects in the vehicle'senvironment), and power system 174 (for example, a battery and/or gas ordiesel powered engine) in order to control the movement, speed, etc. ofvehicle 100 in accordance with the instructions 132 of memory 130 in anautonomous driving mode which does not require or need continuous orperiodic input from a passenger of the vehicle. Again, although thesesystems are shown as external to computing device 110, in actuality,these systems may also be incorporated into computing device 110, againas an autonomous driving computing system for controlling vehicle 100.

The computing device 110 may control the direction and speed of thevehicle by controlling various components. By way of example, computingdevice 110 may navigate the vehicle to a drop-off location completelyautonomously using data from the map data and navigation system 168.Computing devices 110 may use the positioning system 170 to determinethe vehicle's location and perception system 172 to detect and respondto objects when needed to reach the location safely. In order to do so,computing devices 110 may cause the vehicle to accelerate (e.g., byincreasing fuel or other energy provided to the engine by accelerationsystem 162), decelerate (e.g., by decreasing the fuel supplied to theengine, changing gears, and/or by applying brakes by deceleration system160), change direction (e.g., by turning the front or rear wheels ofvehicle 100 by steering system 164), and signal such changes (e.g., bylighting turn signals of signaling system 166). Thus, the accelerationsystem 162 and deceleration system 160 may be a part of a drivetrainthat includes various components between an engine of the vehicle andthe wheels of the vehicle. Again, by controlling these systems,computing devices 110 may also control the drivetrain of the vehicle inorder to maneuver the vehicle autonomously.

As an example, computing device 110 may interact with decelerationsystem 160 and acceleration system 162 in order to control the speed ofthe vehicle. Similarly, steering system 164 may be used by computingdevice 110 in order to control the direction of vehicle 100. Forexample, if vehicle 100 configured for use on a road, such as a car ortruck, the steering system may include components to control the angleof wheels to turn the vehicle. Signaling system 166 may be used bycomputing device 110 in order to signal the vehicle's intent to otherdrivers or vehicles, for example, by lighting turn signals or brakelights when needed.

Navigation system 168 may be used by computing device 110 in order todetermine and follow a route to a location. In this regard, thenavigation system 168 and/or data 134 may store map data, e.g., highlydetailed maps that computing devices 110 can use to navigate or controlthe vehicle. As an example, these maps may identify the shape andelevation of roadways, lane markers, intersections, crosswalks, speedlimits, traffic signal lights, buildings, signs, real time or historicaltraffic information, vegetation, or other such objects and information.The lane markers may include features such as solid or broken double orsingle lane lines, solid or broken lane lines, reflectors, etc. A givenlane may be associated with left and right lane lines or other lanemarkers that define the boundary of the lane. Thus, most lanes may bebounded by a left edge of one lane line and a right edge of another laneline. As noted above, the map data may store known traffic or congestioninformation and/or and transit schedules (train, bus, etc.) from aparticular pickup location at similar times in the past. Thisinformation may even be updated in real time by information received bythe computing devices 110.

FIG. 2 is an example of map data 200. As shown, the map data 200includes the shape, location, and other characteristics of road 210,road 220, road 230, road 240, and road 250. Map data 200 may includelane markers or lane lines, such as lane line 211 for road 210. The lanelines may also define various lanes, for example lane line 211 defineslanes 212, 214 of road 210. As alternative to lane lines or markers,lanes may also be inferred by the width of a road, such as for roads220, 230, 240, 250. The map data 200 may also include information thatidentifies the direction of traffic and speed limits for each lane aswell as information that allows the computing devices 110 to determinewhether the vehicle has the right of way to complete a particular typeof maneuver (i.e. complete a turn, cross a lane of traffic orintersection, etc.).

Map data 200 may also include relationship information between roads210, 220, 230, 240, and 250. For example, map data 200 may indicate thatroad 210 intersects road 220 at intersection 219, that road 220intersects road 230 at intersection 229, that roads 230, 240, and 250intersect at intersection 239, and that road 250 intersects road 210 atintersection 259.

Map data 200 may further include signs and markings on the roads withvarious characteristics and different semantic meanings. As shown, mapdata 200 includes traffic light 216 for road 210 and pedestrian crossing218 across road 210. Map data 200 also includes stop sign 260. The mapdata 200 may additionally include other features such as curbs,waterways, vegetation, etc.

In addition, map data 200 may include various buildings or structures(such as points of interests) and the type of these buildings orstructures. As shown, map data 200 depicts building 270 on road 210. Forexample, map data 200 may include that the type of the building 270 isan airport, train station, stadium, school, church, hospital, apartmentbuilding, house, etc. In this regard, the type of the building 270 maybe collected from administrative records, such as county records, ormanually labeled by a human operator after reviewing aerial images. Mapdata 200 may include additional information on building 270, such as thelocations of entrances and/or exits.

Map data 200 may also store predetermined stopping areas, such as aparking lot 280. In this regard, such areas may be hand-selected by ahuman operator or learned by a computing device over time. Map data 200may include additional information about the stopping areas, such as thelocation of entrance 282 and exit 284 of parking lot 280, and thatentrance 282 connects to road 240, while exit 284 connects to roads 230and 250.

In some examples, map data 200 may further include zoning information.For instance, the zoning information may be obtained from administrativerecords, such as county records. As such, information on the roads mayinclude indication that it is within a residential zone, a school zone,a commercial zone, etc.

The map data may further include location coordinates (examples of whichare shown in FIG. 7), such as GPS coordinates of the roads 210, 220,230, 240, and 250, intersections 219, 229, 239, and 259, lane line 211,lanes 212 and 214, traffic light 216, pedestrian crossing 218, stop sign260, building 270 and its entrance 272, parking lot 280 and its entrance282 and exit 284.

Although the detailed map data is depicted herein as an image-based map,the map data need not be entirely image based (for example, raster). Forexample, the detailed map data may include one or more roadgraphs orgraph networks of information such as roads, lanes, intersections, andthe connections between these features. Each feature may be stored asgraph data and may be associated with information such as a geographiclocation and whether or not it is linked to other related features, forexample, a stop sign may be linked to a road and an intersection, etc.In some examples, the associated data may include grid-based indices ofa roadgraph to allow for efficient lookup of certain roadgraph features.

The perception system 172 also includes one or more components fordetecting objects external to the vehicle such as other vehicles,obstacles in the roadway, traffic signals, signs, trees, etc. Forexample, the perception system 172 may include one or more LIDARsensor(s) 180, camera sensor(s) 182, and RADAR sensor(s) 184. Theperception system 172 may include other sensors, such as SONARdevice(s), gyroscope(s), accelerometer(s), and/or any other detectiondevices that record data which may be processed by computing devices110. The sensors of the perception system may detect objects and theircharacteristics such as location, orientation, size, shape, type (forinstance, vehicle, pedestrian, bicyclist, etc.), heading, and speed ofmovement, etc. The raw data from the sensors and/or the aforementionedcharacteristics can be quantified or arranged into a descriptivefunction, vector, and or bounding box and sent for further processing tothe computing devices 110 periodically and continuously as it isgenerated by the perception system 172. As discussed in further detailbelow, computing devices 110 may use the positioning system 170 todetermine the vehicle's location and perception system 172 to detect andrespond to objects when needed to reach the location safely.

For instance, FIG. 3 is an example external view of vehicle 100. In thisexample, roof-top housing 310 and dome housing 312 may include a LIDARsensor as well as various cameras and RADAR units. In addition, housing320 located at the front end of vehicle 100 and housings 330, 332 on thedriver's and passenger's sides of the vehicle may each store a LIDARsensor. For example, housing 330 is located in front of driver door 350.Vehicle 100 also includes housings 340, 342 for RADAR units and/orcameras also located on the roof of vehicle 100. Additional RADAR unitsand cameras (not shown) may be located at the front and rear ends ofvehicle 100 and/or on other positions along the roof or roof-top housing310. Vehicle 100 also includes many features of a typical passengervehicle such as doors 350, 352, wheels 360, 362, etc.

Once a nearby object is detected, computing devices 110 and/orperception system 172 may determine the object's type, for example, atraffic cone, pedestrian, a vehicle (such as a passenger car, truck,bus, etc.), bicycle, etc. Objects may be identified by various modelswhich may consider various characteristics of the detected objects, suchas the size of an object, the speed of the object (bicycles do not tendto go faster than 40 miles per hour or slower than 0.1 miles per hour),the heat coming from the bicycle (bicycles tend to have rider that emitheat from their bodies), etc. In addition, the object may be classifiedbased on specific attributes of the object, such as informationcontained on a license plate, bumper sticker, or logos that appear onthe vehicle.

For instance, sensor data (examples of which are shown in FIG. 7)collected by one or more sensors of the perception system 172 may bestored in data of computing device 110 of vehicle 100. Referring to FIG.2, vehicle 100 may have driven past stop sign 260 in the past, and havestored the detected values of stop sign 260 by LIDAR sensor(s) 180 indata 134 of memory 130. In this example, the detected values mayinclude, for example, that when vehicle 100 is at location [x_b, y_b](which may for example correspond to driving in road 230 towardsintersection 239 and was 10 m away from reaching intersection 239), thestop sign 260 was detected to be at location [x4, y4] and at a 30° anglefrom a front of vehicle 100 (which may for example correspond to whenvehicle 100 is 8 m away from stop sign 260 on road 230 driving towardsintersection 239). As described in detail below with respect to theexample methods, these stored sensor data may be used for performancechecks on the various systems of the vehicle 100. In other examples,sensor data collected by the perception system of a reference vehiclemay be stored in computing device 110 of vehicle 100. In other examples,such sensor data may be stored remotely on a server or a storage system.

Computing device 110 may further store threshold values (some of whichare shown in FIG. 8) for various components of vehicle 100. For example,computing device 110 may store a threshold minimum tire pressure fortires of vehicle 100. For another example, computing device 110 maystore threshold alignment angles for tires of vehicle 10. For yetanother example, computing device 110 may store a threshold stoppingdistance at a particular speed for a brake of vehicle 100.

The one or more computing devices 110 of vehicle 100 may also receive ortransfer information to and from other computing devices, for instanceusing wireless network connections 156. The wireless network connectionsmay include, for instance, BLUETOOTH®, Bluetooth LE, LTE, cellular, nearfield communications, etc. and various combinations of the foregoing.FIGS. 4 and 5 are pictorial and functional diagrams, respectively, of anexample system 400 that includes a plurality of computing devices 410,420, 430, 440 and a storage system 450 connected via a network 460.System 400 also includes vehicle 100, and vehicle 100A which may beconfigured similarly to vehicle 100. Although only a few vehicles andcomputing devices are depicted for simplicity, a typical system mayinclude significantly more.

As shown in FIG. 4, each of computing devices 410, 420, 430, 440 mayinclude one or more processors, memory, data and instructions. Suchprocessors, memories, data and instructions may be configured similarlyto one or more processors 120, memory 130, data 134, and instructions132 of computing device 110.

The network 460, and intervening nodes, may include variousconfigurations and protocols including short range communicationprotocols such as BLUETOOTH®, Bluetooth LE, the Internet, World WideWeb, intranets, virtual private networks, wide area networks, localnetworks, private networks using communication protocols proprietary toone or more companies, Ethernet, WiFi and HTTP, and various combinationsof the foregoing. Such communication may be facilitated by any devicecapable of transmitting data to and from other computing devices, suchas modems and wireless interfaces.

In one example, one or more computing devices 410 may include a serverhaving a plurality of computing devices, e.g., a load balanced serverfarm, that exchange information with different nodes of a network forthe purpose of receiving, processing and transmitting the data to andfrom other computing devices. For instance, one or more computingdevices 410 may include one or more server computing devices that arecapable of communicating with one or more computing devices 110 ofvehicle 100 or a similar computing device of vehicle 100A as well asclient computing devices 420, 430, 440 via the network 460. For example,vehicles 100 and 100A may be a part of a fleet of vehicles that can bedispatched by server computing devices to various locations. In thisregard, the vehicles of the fleet may periodically send the servercomputing devices location information provided by the vehicle'srespective positioning systems and the one or more server computingdevices may track the locations of the vehicles.

As mentioned above, rather than saving sensor data detecting varioustraffic features on computing device 110, such sensor data mayadditionally or alternatively be stored on server computing device 410.Likewise, threshold values for components of vehicle 100 may likewise bestored on server computing device 410.

In addition, server computing devices 410 may use network 460 totransmit and present information to a user, such as user 422, 432, 442on a display, such as displays 424, 434, 444 of computing devices 420,430, 440. In this regard, computing devices 420, 430, 440 may beconsidered client computing devices.

As shown in FIG. 5, each client computing device 420, 430, 440 may be apersonal computing device intended for use by a user 422, 432, 442, andhave all of the components normally used in connection with a personalcomputing device including a one or more processors (e.g., a centralprocessing unit (CPU)), memory (e.g., RAM and internal hard drives)storing data and instructions, a display such as displays 424, 434, 444(e.g., a monitor having a screen, a touch-screen, a projector, atelevision, or other device that is operable to display information),and user input devices 426, 436, 446 (e.g., a mouse, keyboard,touchscreen or microphone). A user, such as user 422, 432, 442, may sendinformation, such as pickup or drop-off requests, to server computingdevices 410, using user input devices 426, 436, 446 of computing devices420, 430, 440. The client computing devices may also include a camerafor recording video streams, speakers, a network interface device, andall of the components used for connecting these elements to one another.

Although the client computing devices 420, 430, and 440 may eachcomprise a full-sized personal computing device, they may alternativelycomprise mobile computing devices capable of wirelessly exchanging datawith a server over a network such as the Internet. By way of exampleonly, client computing device 420 may be a mobile phone or a device suchas a wireless-enabled PDA, a tablet PC, a wearable computing device orsystem, or a netbook that is capable of obtaining information via theInternet or other networks. In another example, client computing device430 may be a wearable computing system, shown as a wrist watch in FIG.4. As an example the user may input information using a small keyboard,a keypad, microphone, using visual signals with a camera, or a touchscreen.

In some examples, client computing device 440 may be remote operatorwork station used by an administrator to provide remote operatorservices to users such as users 422 and 432. For example, a remoteoperator 442 may use the remote operator work station 440 to communicatevia a telephone call or audio connection with users through theirrespective client computing devices and/or vehicles 100 or 100A in orderto ensure the safe operation of vehicles 100 and 100A and the safety ofthe users as described in further detail below. Although only a singleremote operator work station 440 is shown in FIGS. 4 and 5, any numberof such work stations may be included in a typical system.

Storage system 450 may store various types of information as describedin more detail below. This information may be retrieved or otherwiseaccessed by a server computing device, such as one or more servercomputing devices 410, in order to perform some or all of the featuresdescribed herein. For example, the information may include user accountinformation such as credentials (e.g., a username and password as in thecase of a traditional single-factor authentication as well as othertypes of credentials typically used in multi-factor authentications suchas random identifiers, biometrics, etc.) that can be used to identify auser to the one or more server computing devices. The user accountinformation may also include personal information such as the user'sname, contact information, identifying information of the user's clientcomputing device (or devices if multiple devices are used with the sameuser account), as well as age information, health information, and userhistory information about how long it has taken the user to enter orexit vehicles in the past as discussed below.

The storage system 450 may also store routing data for generating andevaluating routes between locations. For example, the routinginformation may be used to estimate how long it would take a vehicle ata first location to reach a second location. In this regard, the routinginformation may include map data, not necessarily as particular as thedetailed map data 200 described above, but including roads, as well asinformation about those road such as direction (one way, two way, etc.),orientation (North, South, etc.), speed limits, as well as trafficinformation identifying expected traffic conditions, etc.

As mentioned above, rather than saving sensor data detecting varioustraffic features on computing device 110 or server computing device 410,such sensor data may additionally or alternatively be stored on storagesystem 450. Likewise, threshold values for components of vehicle 100 maylikewise be stored on storage system 450.

The storage system 450 may also store information which can be providedto client computing devices for display to a user. For instance, thestorage system 450 may store predetermined distance information fordetermining an area at which a vehicle is likely to stop for a givenpickup or drop-off location. The storage system 450 may also storegraphics, icons, and other items which may be displayed to a user asdiscussed below.

As with memory 130, storage system 450 can be of any type ofcomputerized storage capable of storing information accessible by theserver computing devices 410, such as a hard-drive, memory card, ROM,RAM, DVD, CD-ROM, write-capable, and read-only memories. In addition,storage system 450 may include a distributed storage system where datais stored on a plurality of different storage devices which may bephysically located at the same or different geographic locations.Storage system 450 may be connected to the computing devices via thenetwork 460 as shown in FIG. 4 and/or may be directly connected to orincorporated into any of the computing devices 110, 410, 420, 430, 440,etc.

Example Methods

In addition to the systems described above and illustrated in thefigures, various operations will now be described. It should beunderstood that the following operations do not have to be performed inthe precise order described below. Rather, various steps can be handledin a different order or simultaneously, and steps may also be added oromitted

FIG. 6 illustrates an example situation 600 for performing a pluralityof performance checks on vehicle 100. Various features in FIG. 6 maygenerally correspond to the shape, location, and other characteristicsof features shown in map data 200 of FIG. 2, and labeled as such.Additional features in FIG. 6, including various road users and otherobjects, are described in detail below. Although these examples areuseful for demonstration purposes, they should not be consideredlimiting.

As shown in FIG. 6, vehicle 100 is currently parked curbside in lane 212of road 210, to ensure safe operation on the road, vehicle 100 may needto perform performance checks on its systems. In this regard, vehicle100 may be scheduled to perform the performance checks on its systems ona regular basis, such as every day or week, every predetermined numberof kilometers traveled or numbers of trips completed, or some frequencymandated by law. For example, during a previous day, vehicle 100 mighthave completed a number of trips, and upon completing these trips,vehicle 100 has parked roadside by the curb in lane 212. On the currentday, before going on more trips, vehicle 100 may first perform aplurality of performance checks on its various systems. In one instance,some types of performance checks may be performed at higher frequencythan other performance checks. In another instance, the frequency of theperformance checks may depend on the type of vehicle.

In order to perform the plurality of performance checks on the vehicle,a test route may be determined. In this regard, computing device 110 maydetermine the test route based on a location of the vehicle, map data,and the types of performance checks to be performed. For instance,computing device 110 may determine that vehicle 100 is currently parkedby the curb in lane 212 near intersection 219, and determine, based onmap data 200, a test route nearby so that vehicle 100 does not need todrive to a designated depot or testing center just to perform thesetests. This ensures that the performance checks are performed as soon aspossible, instead of risking operating the vehicle 100 on a long driveto the designated testing center, and ensures a more efficient use ofresources, including fuel.

Computing device 110 may determine the test route further based on thetypes of performance checks that need to be performed in order tocomplete the plurality of performance checks. For instance, a list ofrequired performance checks, including the type of each requiredperformance check and frequency for each required performance check, maybe stored on computing device 110. Additionally or alternatively, thelist of required performance checks may be stored on server computingdevice 410 and/or storage system 450 accessible by computing device 110.For example, the list of required performance checks may include itemssuch as “perform a sensor check using a stored traffic light detectionat least once per 24 hours,” “perform a map check using a stop signstored in map data at least once per month,” “perform a component checkon all four tires at least once per week,” etc. In one aspect, computingdevice 110 may select a plurality of segments, such as a plurality ofroad segments, using map data and stored sensor data, where each of theplurality of road segments is selected for performing one or more of therequired performance checks. Computing device 110 may then connect theplurality of road segments, and connect the location of the vehicle toone of the plurality of road segments to determine a test route in orderto allow the vehicle to perform the plurality of performance checks.

For instance, for a sensor check, computing device 110 may select asegment for a test route so that sensor data can be collected to comparewith stored values of previous detections of traffic features orstationary objects. For example, as shown in FIG. 6, computing device110 may determine that traffic light 216 and pedestrian crossing 218nearby vehicle 100 are stored as previously detected traffic featuresnear vehicle 100. As such, a sensor check on one or more detectionsystems may be performed by comparing newly detected values to thesestored values. Therefore, computing device 110 may determine thatsegment 610 beginning at the current location of vehicle 100 and a rightturn from lane 212 to road 220 may be used for performing the sensorcheck.

For a map check, computing device 110 may select a segment for a testroute so that sensor data can be collected to compare with storedlocations and/or orientations of traffic features or stationary objectsin map data. For example, as shown in FIG. 6, computing device 110 maydetermine that stop sign 260 is stored in map data 200 as a trafficfeature. As such, a map check on the map data 200 stored in navigationsystem may be performed by comparing newly detected location andorientation of the stop sign 260 with the stored location and/ororientation of the stop sign 260 in map data 200. Therefore, computingdevice 110 may determine that segment 640 including a portion of road230 near stop sign 260 may be used for performing the map check.

For a component check, computing device 110 may select a segment for atest route where a particular vehicle maneuver may be performed. Forexample, as shown in FIG. 6, computing device 110 may determine that aleft turn may be performed at intersection 229. As such, a componentcheck on the brake, wheel alignment, and left turn signal may beperformed at intersection 229 while vehicle 100 performs the left turn.Therefore, computing device 110 may determine that segment 620 includinga portion of road 220 and a left turn at intersection 229 to road 230can be used for performing the component check.

In some instances, computing device 110 may determine that more than onesegment is needed for performing a particular type of performance check.In this regard, a human operator may manually a list of items for a testroute. Alternatively or additionally, a list of items required for atest route may be stored on computing devices 110, and/or stored onserver computing device 410 and/or storage system 450 accessible bycomputing device 110. For example, the list of items required for a testroute may include items such as “five or more traffic lights on the testroute,” “one multipoint turn on the test route,” etc. For example, asshown in FIG. 6, computing device 110 may determine that a componentcheck for the reverse signal requires maneuvers such as back-in orparallel parking or multi-point turns. As such, computing device maydetermine that segment 650 including parking lot 280 may be used forperforming the component check on the reverse signal.

In other instances, the segments of test routes selected for each typeof performance check may be the same or have overlapping portions, or inother words, a given segment may be used for performing multiple typesof performance checks. For example, in addition for sensor check,segment 610 may also be used for map check, since the location and/ororientation of traffic features such as traffic light 216 and pedestriancrossing 218 are stored in map data 200. For another example, segment610 may also be used for a component check on the brake, wheelalignment, and right turn signal.

Computing device 110 may select additional segments of test routeconnecting the various segments selected for the particular types ofperformance checks. For example, computing device 110 may determine thatsegment 630 may be needed to connect segment 620 and segment 640. Assuch, an example test route may include segments 610, 620, 630, 640, and650.

Further, where applicable, computing device 110 may store thecorresponding or associated performance checks to be performed usingsensor data from each segment of the test route. For example, computingdevice 110 may associate segment 610 with a sensor check, a map checkusing traffic light 216, pedestrian crossing 218, and a component checkon wheel alignment and right-turn signal. For another example, computingdevice 110 may associate segment 620 with a component check on left-turnsignal and wheel alignment. For still another example, computing device110 may associate segment 640 with a sensor check and a map check usingstop sign 260. For yet another example, computing device 110 may notassociate any check with segment 630.

Computing device 110 may determine the test route further based onadditional requirements for test routes. One example requirement may bethat one or more segments of the test route must have below a thresholdtraffic volume. In this regard, computing device 110 may receivehistorical or real-time traffic data from a database. Another examplerequirement may be that one or more segments of the route must have aspeed limit below or above a threshold speed limit. In this regard,computing device 110 may determine speed limits of various roads basedon map data 200. Yet another example requirement may be that one or moresegments of the route must not be performed in certain areas, such as aschool zone. In this regard, computing device 110 may determine zoninginformation based on map data 200. Still another example requirement maybe that particular types of maneuvers must be performed in a parkinglot. For example, as shown in FIG. 6, although multi-point turnmaneuvers may also be performed on road 230, parking lot 280 may bechosen based on a requirement that multi-point turns be made in parkinglots. Additional example requirements may be that the test route mustinclude multiple distinct traffic lights, one or more cul-de-sacs forperforming multipoint turns, and a stored traffic feature in adesignated depot or testing center for the vehicle 100.

The test route need not be a closed loop. For example, as shown in FIG.6, computing device 110 may determine to go on a next segment at the endof segment 650, instead of returning to the beginning of the test route.In other examples, the test route may be a closed loop, for example,computing device 110 may determine an additional segment connecting theend of segment 650 back to the beginning of segment 610. In exampleswhere the test route is a closed loop, the plurality of performancechecks may be repeated in order to collect more sets of sensor data,which for example may be averaged to obtain more accurate results.

The test route may be stored so that the test route can be used again bythe vehicle at a later time to perform the aforementioned checks. Forinstance, the test route described above may be stored, and if vehicle100 happens to be around the area when the plurality of performancechecks need to be performed again, computing device 110 may simply usethe stored test route, instead of determining a new test route. Foranother instance, based on the performance checks need to be performedagain, computing device 110 may use some but not all the segments of thestored test route.

Once the test route is determined, computing device 110 may control thevehicle 100 to drive along the test route. While doing so, theperception system 172 and/or computing devices 110 may collect datawhile on the test route including sensor data and component data inorder to perform the aforementioned performance checks. For example,FIG. 7 shows example sensor data 700 collected by various sensors in theperception system 172 while vehicle 100 drives along the test routeshown in FIG. 6. For another example, FIG. 8 shows example componentsdata 800 collected from various components while vehicle 100 drivesalong the test route shown in FIG. 6.

Referring to FIGS. 6 and 7, the collected sensor data may include dataon permanent traffic features or stationary objects, such as trafficlight 216, pedestrian crossing 218, building 270, stop sign 260, andparking lot 280. As shown in FIG. 7, the sensor data may includeinformation such as the detected location and orientation of eachtraffic feature or object, as well as the location of the vehicle 100when the sensor data on that feature or object is taken. For example,while on the test route, when vehicle 100 is at location [x_a, y_a],LIDAR sensor(s) 180 of vehicle 100 may detect traffic light 216 atlocation [x1, y1] and at an angle 25° from vehicle 100, and building 270at location [x3, y3] and at an angle 10° from vehicle 100. For anotherexample, while still on the test route, when vehicle 100 is at location[x_b, y_b], LIDAR sensor(s) 180 of vehicle 100 may detect stop sign 260at location [x4, y4] and at an angle 25° from vehicle 100, and parkinglot 280 at location [x5, y5] and at an angle 25° from vehicle 100.Locations of vehicle 100 during the test route may be determined bynavigation system 168. Although not shown, the LIDAR data may furtherinclude details such as the size and shape of these features or objects.

The stored sensor data may include information such as the previouslydetected location and orientation of each traffic feature or object. Inthis regard, the stored sensor data for a traffic feature or object mayinclude previous detections of the traffic feature or object by sensorsof the vehicle 100 in the past. The stored sensor data for a trafficfeature or object may additionally or alternatively include previousdetections of the traffic feature or object made by sensors of othervehicles. Further, the stored sensor data may include the location ofthe vehicle taking the sensor data when the traffic feature or objectwas detected. The stored sensor data may be stored on computing device110. Additionally or alternatively, the stored sensor data may be storedon server computing device 410 and/or storage system 450 accessible bycomputer device 110.

Although not shown, stored sensor data and collected sensor data mayinclude same type of sensor data taken by multiple sensors, such as bydifferent LIDAR sensors mounted at different locations in or on vehicle100. Further, stored sensor data and collected sensor data may includedifferent types of sensor data, such as camera data. Each type of sensordata may include similar information as LIDAR data, such as detectedlocation and orientation of each traffic feature or object, and thelocation of the vehicle 100 when the sensor data on that feature orobject is taken. In addition, each type of sensor data may includefurther details such as the size, shape, color of these features orobjects.

Although not shown, the collected sensor data may further include dataon temporary or moving traffic features and/or objects, such as vehicle100A, vehicle 100B, traffic cone 670 and pedestrian 680. For example,while at location [x_c, y_c] of the test route, LIDAR sensor(s) 180 ofvehicle 100 may detect vehicle 100A at location [x6, y6] at a 15° anglefrom the front of the vehicle 100. At or around the same time, camerasensor(s) 182 and RADAR sensor 184 may also each detect vehicle 100A atlocation [x6, y6] at a 15° angle. For example, the camera data mayfurther include the color of vehicle 100A, and RADAR data may furtherinclude speed of vehicle 100A.

Referring to FIGS. 6 and 8, component data may be collected on variouscomponents of vehicle 100. For example as shown in FIG. 8, tirepressures may be collected for all four tires of vehicle 100. Foranother example, wheel alignment data may be collected on all fourwheels of vehicle 100. The wheel alignment data may include camberangle, caster angle, and toe angle for each wheel. For still anotherexample, data on brake of vehicle 100 may be collected. For instance,stopping distance at a specific speed such as 100 km/hr may be measured.For yet another example, responsiveness of various lights, such as theturn and reverse signals, as well as night light, can be turned on andoff.

During or after the vehicle completes the test route, the plurality ofperformance checks may be performed by analyzing the collected data.Computing devices 110 may perform the plurality of performance checks byanalyzing the collected data in real time while vehicle 100 navigatesthrough the test route, or store the collected data in memory 130 sothat computing device 110 may perform the checks after completing thetest route. Additionally or alternatively, the collected data may beuploaded to server computing device 410 or storage system 450 so thatserver computing device 410 may perform the plurality of performancechecks. Having computing device 110 perform the checks may providegreater efficiency, since uploading collected data to server computingdevice 410 or storage system 450 may be time consuming.

For a sensor check, detected characteristics of a traffic feature orobject collected during the test route may be compared with previouslydetected or stored characteristics of the traffic feature or object. Asensor may satisfy the sensor check when the characteristics collectedduring the test route match the previously detected or storedcharacteristics by the same sensor, and not satisfy the sensor checkwhen the characteristics collected during the test route do not matchthe previously detected or stored characteristics. For instance,referring to FIG. 7, collected LIDAR data from LIDAR sensor(s) 180 maybe compared to stored LIDAR values from a previous detection by LIDARsensor(s) 180. For example, computing device 110 may determine thatdetected location for each of traffic light 216, building 270, stop sign260 and parking lot 280 are identical to stored LIDAR values, butdetected orientation of each is offset by a 5° angle. As such, computingdevice 110 may determine that LIDAR sensor(s) 180 does not satisfy thesensor check.

FIG. 9 shows an example situation 900 illustrating an example sensorcheck. Various features in FIG. 9 may generally correspond to the shape,location, and other characteristics of features shown in map data 200 ofFIG. 2, and labeled as such. Additional features in FIG. 9, includingvarious road users and other objects, are described in detail below.Although these examples are useful for demonstration purposes, theyshould not be considered limiting.

As shown in FIG. 9, while vehicle 100 is at location [x_b, y_b], LIDARsensor(s) 180 detects stop sign 260 at a location [x4, y4] andorientation of 25° angle with respect to a front right corner of vehicle100. However, the stored LIDAR values for the stop sign 260 includelocation [x4, y4] and orientation of 30° angle with respect to a frontright corner of vehicle 100. This may be due to a movement of the LIDARsensor(s) 180 from its previous position when the stored LIDAR valueswere taken. For example, a pedestrian might have accidentally touchedthe LIDAR sensor(s) 180 when passing by vehicle 100 while vehicle 100was parked curbside in lane 212. As such, this rotation causes a −5°angle offset for all detections made by LIDAR sensor(s) 180.

Additionally or alternatively, computing device 110 may compare thelocation and/or orientation of traffic features and/or objects detectedin the collected sensor data with the location and/or orientation oftraffic features and/or objects stored in map data 200. For example asshown in FIG. 7, computing device 110 may compare location [x1, y1] fortraffic light 216 detected in collected LIDAR data with location [x1,y1] stored in map data 200, compare location [x3, y3] for building 270detected in collected LIDAR data with location [x3, y3] stored in mapdata 200, compare location [x4, y4] for stop sign 260 detected incollected LIDAR data with location [x4, y4] stored in map data 200,compare location [x5, y5] for parking lot 280 detected in collectedLIDAR data with location [x5, y5] stored in map data 200, and concludethat LIDAR sensor(s) 180 pass the sensor check. In this regard,computing device 110 may compare collected sensor data with map data 200for some or all traffic features and/or objects detected during the testroute.

In some instances, computing device 110 may determine that a sensor maystill pass a sensor test if the differences between the stored andcollected sensor data are within a predetermined range. For instance,computing device 110 may determine that LIDAR sensor(s) 180 may stillpass the sensor test if the difference in stored and detectedorientation for a detected object is within a 10° range.

Computing device 110 may determine one or more corrections for one ormore sensors that fails the sensor test. For example, for LIDARsensor(s) 180, computing device 110 may determine a +5° correction forall orientation values detected by LIDAR sensor(s) 180. For instance,computing device 110 may add 5° to the detected 25° angle for stop sign260.

Another sensor check may include comparing collected sensor data fromvarious sensors of a same type for a detected object. For instance, ifLIDAR sensor(s) 180 include multiple sensors have overlapping fields ofviews, computing device 110 may compare the LIDAR point cloud fortraffic light 216 collected by a first sensor with the LIDAR point cloudfor traffic light 216 collected by a second sensor. Such sensor error ofthe second sensor may be caused by any of a number of factors, such asdamage by another road user, or due to environmental factors such asextreme temperature or humidity. Computing device 110 may determinethat, if the two LIDAR point clouds match substantially, such as by 90%or some other threshold, then both the first and second sensors pass thesensor check.

Still another sensor check may include determining a resolution or fieldof view captured by a sensor. For example, if collected LIDAR data forLIDAR sensor(s) 180 has a smaller field of view than the stored LIDARdata, computing device 110 may further determine that LIDAR sensor(s)180 has failed the sensor check. In some instances, computing device 110may determine that LIDAR sensor(s) 180 may still pass the sensor test ifthe difference between field of view of the collected LIDAR data duringtest route and field of view of the stored LIDAR data is within apredetermined threshold difference. For another example, if collectedcamera data for camera sensor(s) 182 has a lower resolution than thestored camera data, computing device 110 may further determine thatcamera sensor(s) 182 has failed the sensor check. In some instances,computing device 110 may determine that camera sensor(s) 182 may stillpass the sensor test if the difference between resolution of thecollected camera data during test route and resolution of stored cameradata is within a predetermined threshold difference. Such changes inresolution or field of view may be caused by any of a number of factors,such as damage by another road user, or due to environmental factorssuch as extreme temperature or humidity.

Yet another sensor check may include determining whether a sensorproduces unreasonable sensor data. For example, computing device 110 maydetermine that camera data produced by camera sensor(s) 182 are allgreen, and conclude that camera sensor(s) 182 fail the sensor check. Foranother example, computing device 110 may determine that LIDAR sensor(s)180 produces empty point clouds, and conclude that LIDAR sensor(s) 180fail the sensor check. Such changes in resolution or field of view maybe caused by any of a number of factors, such as damage by another roaduser, or due to environmental factors such as extreme temperature orhumidity.

For another instance, for a map check, a location or orientation of adetected traffic feature may be compared with the location and/ororientation of a previously detected or stored traffic feature stored inmap data of the vehicle. The map data may satisfy the map check when thelocation and/or orientation of detected traffic features during the testroute match the location and/or orientation of traffic feature stored inthe map data. For instance, referring to FIG. 7, locations of trafficfeatures detected by LIDAR sensor(s) 180 may be compared to locationsstored in map data 200. For example, computing device 110 may determinethat detected location by LIDAR sensor(s) 180 for each of traffic light216, building 270, stop sign 260 and parking lot 280 are identical tolocations stored in map data 200, but that pedestrian crossing 218 isnot detected by LIDAR sensor(s) 180.

When a difference between the map data 200 and collected sensor data ona traffic feature is detected, computing device 110 may furtherdetermine whether the difference was due to an error in the map data 200or an error in the collected sensor data. For example, computing device110 may determine that, since pedestrian crossing is not a 3D structureand that the field of view of LIDAR sensor(s) 180 does not includeground level, LIDAR sensor(s) 180 cannot detect pedestrian crossing 218,and therefore the difference does not indicate an error in map data 200.In such cases, computing device 110 may further confirm by comparing thelocation stored in map data 200 with collected sensor data from anothersensor, such as camera sensor(s) 182. For example, computing device 110may determine that the location for pedestrian crossing 218 in map data200 matches the location detected by camera sensor(s) 182.

In some instances, computing device 110 device may determine that anupdate needs to be made for map data 200. Referring to FIG. 9, whichshows the example situation 900 further illustrating an example mapcheck. As shown, while at location [x_b, y_b], LIDAR sensor(s) 180 ofvehicle 100 detects a no-enter sign 910 near exit 284 of parking lot280. However, map data 200 does not include data on a no-enter sign atthis location. As such, computing device 110 may determine to update mapdata 200 with the detected location of no-enter sign 910.

In addition or alternatively, computing device 110 may determine that,even if some error exists, the map data may still pass a map test if athreshold number or percentage of traffic features stored in the mapdata have locations matching the detected locations from the collectedsensor data. For instance, computing device 110 may determine that mapdata 200 may still pass the map test if at least five or at least 80% ofthe stored features have locations matching the collected sensor data.For example, since locations for traffic light 216, pedestrian crossing218, building 270, stop sign 260, and parking lot 280 match that ofcollected LIDAR data, even though location for the no-enter sign 910 wasmissing, computing device 110 may still determine that map data 200 maypass the map test.

For another instance, for a component check, the one or moremeasurements related to a component of the vehicle may be compared withpredetermined requirements. The component may satisfy the componentcheck when the one or more measurements satisfy predeterminedrequirements. For example, the predetermined requirements may be storedin computing device 110, or alternatively or additionally stored onserver computing device 410 and/or storage system 450.

In some instances, a component may satisfy a component check if ameasurement meets a predetermined threshold value. For example,referring to FIG. 8, a predetermined minimum threshold of 35 psi may bestored for tires of vehicle 100. As shown, since the front left tire,the rear left tire, and the rear right tire each meets the predeterminedminimum threshold, these tires satisfy the component check. However,since front right tire has a pressure of only 20 psi, the front righttire fails the component check.

Additionally of alternatively, a component may satisfy a component checkif a measurement is within a predetermined range of values. For example,referring to FIG. 8, predetermined alignment angles may be set for thetires of vehicle 100, which include camber, caster, and toe angles.Since each of the tires of vehicle 100 have alignment angles withinthese predetermined ranges, each of the tires of vehicle 100 passes thecomponent check.

Additionally of alternatively, a component may satisfy a component checkif a measurement indicates that the component has a predetermined levelof responsiveness. For example, referring to FIG. 8, the predeterminedlevel of responsiveness may be set as a binary (responsive or not) foreach of the left turn, right turn, reverse, and brake signal lights, aswell as the headlight. As shown, since left turn, right turn, reverse,and brake signal lights are all responsive, computing device 110 maydetermine that they each pass the component check. However, sinceheadlight is unresponsive, computing device 110 may determine that theheadlight fails the component check.

For another example, again referring to FIG. 8, the predetermined levelor responsiveness may be set as a predetermined level of delay. Asshown, for the brake, a predetermined stopping distance at a specificspeed, such as 100 km/hr, may be set for vehicle 100. As such, since themeasured stopping distance for vehicle 100 is 19 m, which is below thepredetermined stopping distance of 20 m, computing device 110 maydetermine that the brake passes the component check.

Once the plurality of performance checks are completed, computingdevices 110 may select an operation mode for vehicle 100. Modes foroperation may include, for example, task designations (passenger ornon-passenger tasks). Modes of operation may further include variouslimits, such as limits on speeds, distance, geographic area, orenvironmental conditions (such as weather, day/night). Modes foroperation may also include an inactive mode where the vehicle is pulledover or parked after completing the plurality of performance checks.

Computing device 110 may determine an operation mode based on resultsfrom the plurality of performance checks. For example, an operation modemay only be selected if a threshold number of percentage of performancechecks are passed. For another example, an operation mode may only beselected if a specific set of performance checks are passed, such as aset of performance checks specific to driving at night or during poorvisibility, which may include performance checks such as the sensorchecks described above, and component checks involving signal lights andheadlight, etc.

Computing device 110 may determine that one or more operation modescannot be selected based on specific failures. For example, computingdevice 110 may determine that, if stopping distance at 100 km/hr forvehicle 100 is above 20 m, modes of operation involving driving at aspeed of 100 km/hr or greater cannot be selected. For another example,computing device 110 may determine that, if less than 80% of the sensorsin the perception system 172 fail the sensor test, operation modeinvolving driving at night or certain weather conditions cannot beselected. For still another example, computing device 110 may determinethat, if one or more tires has a tire pressure below 35 psi, modes ofoperation involving passenger tasks cannot be selected.

Computing device 110 may select an operation mode further based on otherfactors, such as traffic law requirements and the type of vehicle. Forexample, traffic law may require a vehicle to have operating turnsignals. As such, computing device 110 may select the inactive mode ifany of the turn signals is unresponsive. For another example, computingdevice 110 may select an operation mode with a limit on distance onlyfor compact vehicles with below normal tire pressures, and select aninactive operation mode for trucks with below normal tire pressures.

Once an operation mode is selected, computing device 110 may operatevehicle 100 in the selected operation mode. For example, operating inthe selected operation mode may include operating according to limits ofthe mode of operation, such as limits on speed, distance, geographicarea, environmental condition. For another example, operating in theselected mode may include whether to determining whether to acceptpassenger or non-passenger tasks.

Operating in the selected operation mode may further include using thedetermined corrections for one or more sensors. For example, asdescribed with respect to FIGS. 7 and 9, when operating vehicle 100,computing device 110 may apply a correction of +5° to sensor datadetected by LIDAR sensor(s) 180.

Operating in the selected operation mode may further include using theupdated map data. For example, as described with respect to FIGS. 7 and9, when operating vehicle 100, computing device 110 may use updated mapdata 200 including the no-enter sign 910.

Operation modes may also be selected for a plurality of vehicles by aremote system, such as a fleet management system. For example, servercomputing device 410 may manage a fleet of vehicles including vehicle100, 100A, 100B. In this regard, sensor data and component datacollected by various vehicles in the fleet, such as vehicle 100, 100A,100B may be uploaded to server computing device 410. Server computingdevice 410 may compare the collected sensor data from each vehicle tostored sensor values from previous detections. Server computing device410 may also compare the collected components data with storedpredetermined requirements. In some instances, the plurality ofperformance checks may be performed by computing device of each vehicle,and only the results (pass/fail) are uploaded to server computing device410. Sever computing device 410 may then designate modes of operationsfor subsets of vehicles of the plurality of vehicles based on theplurality of performance checks as described above, such as based onpassing a threshold number or percentage of performance checks,particular sets of performance checks, other factors such as type ofvehicle or traffic law, etc. For another example, server computingdevice 410 may designate modes of operations further based on a planneddistribution or demand for the vehicles in the fleet.

FIG. 10 shows an example flow diagram 1000 of an example method forperforming a plurality of performance checks. The example method may beperformed by one or more processors, such as one or more processors 120of computing device 110. For example, processors 120 of computing device110 may receive data and make various determinations as shown in flowdiagram 1000, and control the vehicle 100 based on these determinations.

Referring to FIG. 10, in block 1010, a plurality of performance checksare identified, including a first check for a detection system of aplurality of detection systems of the vehicle and a second check for mapdata. In block 1020, a plurality of road segments are selected based ona location of the vehicle and the plurality of performance checks,wherein each of the plurality of road segments is selected forperforming one or more of the plurality of performance checks. In block1030, a test route is determined for the vehicle by connecting theplurality of road segments and by connecting the location of the vehicleto one of the plurality of road segments. For example, a plurality ofroad segments and a test route may be determined as described inrelation to FIG. 6. In block 1040, the vehicle is controlled along thetest route in an autonomous driving mode. In block 1050, whilecontrolling the vehicle, sensor data are received from the plurality ofdetection systems of a vehicle. For example, sensor data collected on atest route may be received by computing device 110 as described inrelation to FIG. 7.

In block 1060, the plurality of performance checks are performed basedon the received sensor data. For example, one or more sensor checks maybe performed by comparing the collected sensor data with stored previoussensor data. For another example, one or more map checks may beperformed by comparing the collected sensor data with map data. In block1070, an operation mode is selected from a plurality of operation modesfor the vehicle based on results of the plurality of performance checks.For example, the driving mode may be selected based on the resultsmeeting a threshold number or percentage of performance checks. In block1080, the vehicle is operated in the selected operation mode. Forexample, operating in the selected operation mode may include usingcorrections to sensor data or updates to map data.

Unless otherwise stated, the foregoing alternative examples are notmutually exclusive, but may be implemented in various combinations toachieve unique advantages. As these and other variations andcombinations of the features discussed above can be utilized withoutdeparting from the subject matter defined by the claims, the foregoingdescription of the embodiments should be taken by way of illustrationrather than by way of limitation of the subject matter defined by theclaims. In addition, the provision of the examples described herein, aswell as clauses phrased as “such as,” “including” and the like, shouldnot be interpreted as limiting the subject matter of the claims to thespecific examples; rather, the examples are intended to illustrate onlyone of many possible embodiments. Further, the same reference numbers indifferent drawings can identify the same or similar elements.

The invention claimed is:
 1. A system in a vehicle, the systemcomprising: a computing device including one or more processors, theprocessors configured to: identify a plurality of performance checksincluding a first check for a detection system of a plurality ofdetection systems of the vehicle and a second check for map data; selecta plurality of road segments based on a location of the vehicle and theplurality of performance checks, wherein each of the plurality of roadsegments is selected for performing one or more of the plurality ofperformance checks; determine a test route for the vehicle by connectingthe plurality of road segments and by connecting the location of thevehicle to one of the plurality of road segments; control the vehiclealong the test route in an autonomous driving mode; while controllingthe vehicle, receive sensor data from the plurality of detection systemsof the vehicle; perform the plurality of performance checks based on thereceived sensor data; select an operation mode from a plurality ofoperation modes for the vehicle based on results of the plurality ofperformance checks; and operate the vehicle in the selected operationmode.
 2. The system of claim 1, wherein the plurality of road segmentsincludes a first road segment, wherein one or more of the plurality ofperformance checks are performed using one or more traffic features orstationary objects that are detectable along the first road segment. 3.The system of claim 1, wherein the plurality of road segments includes asecond road segment on which a maneuver required for one or more of theplurality of performance checks can be performed.
 4. The system of claim1, wherein the first check includes comparing characteristics of adetected traffic feature with previously detected characteristics of thetraffic feature.
 5. The system of claim 1, wherein the second checkincludes comparing a location of a detected traffic feature with alocation of the detected traffic feature in the map data.
 6. The systemof claim 1, wherein the plurality of performance checks further includesa third check for a component of the vehicle, the third check includescomparing one or more measurements related to the component of thevehicle with a threshold measurement.
 7. The system of claim 1, whereinthe operation mode is selected based on the results satisfying athreshold number of the plurality of performance checks.
 8. The systemof claim 1, wherein the operation mode is selected based on the resultssatisfying one or more set of performance checks of the plurality ofperformance checks.
 9. The system of claim 1, wherein the one or moreprocessors are further configured to: determine one or more correctionsto at least one of the detection systems based on the results of theplurality of performance checks.
 10. The system of claim 9, whereinoperating in the selected operation mode includes using the one or morecorrections.
 11. The system of claim 1, wherein the one or moreprocessors are further configured to: update the map data based on theresults of the plurality of performance checks.
 12. The system of claim11, wherein operating in the selected operation mode includes using theupdated map data.
 13. The system of claim 1, wherein the selectedoperation mode is an inactive mode.
 14. The system of claim 1, furthercomprising: the vehicle.
 15. A method for performing checks on avehicle, the method comprising: identifying, by one or more processorsof a computing device in the vehicle, a plurality of performance checksincluding a first check for a detection system of a plurality ofdetection systems of the vehicle and a second check for map data;selecting, by the one or more processors, a plurality of road segmentsbased on a location of the vehicle and the plurality of performancechecks, wherein each of the plurality of road segments is selected forperforming one or more of the plurality of performance checks;determining, by the one or more processors, a test route for the vehicleby connecting the plurality of road segments and by connecting thelocation of the vehicle to one of the plurality of road segments;controlling, by the one or more processors, the vehicle along the testroute in an autonomous driving mode; while controlling the vehicle,receiving, by the one or more processors, sensor data from the pluralityof detection systems of the vehicle; performing, by the one or moreprocessors, the plurality of performance checks based on the receivedsensor data; selecting, by the one or more processors, an operation modefrom a plurality of operation modes for the vehicle based on results ofthe plurality of performance checks; and operating, by the one or moreprocessors, the vehicle in the selected operation mode.
 16. The methodof claim 15, wherein the plurality of road segments includes a firstroad segment, wherein one or more of the plurality of performance checksare performed using one or more traffic features or stationary objectsthat are detectable along the first road segment.
 17. The method ofclaim 15, wherein the plurality of road segments includes a second roadsegment on which a maneuver required for one or more of the plurality ofperformance checks can be performed.
 18. The method of claim 15, furthercomprising: determining, by the one or more processors, one or morecorrections to at least one of the detection systems based on theresults of the plurality of performance checks, wherein operating in theselected operation mode includes using the one or more corrections. 19.The method of claim 15, further comprising: updating, by the one or moreprocessors, the map data based on the results of the plurality ofperformance checks, wherein operating in the selected operation modeincludes using the updated map data.
 20. The method of claim 15, whereinthe plurality of performance checks are performed at a regular interval.